PROJECT SUMMARY Coronary artery disease remains the leading cause of death worldwide, and more than half of the individuals suffering acute myocardial infarction have no prior symptoms. Coronary calcium scoring with non-contrast CT is increasingly used for cardiovascular risk stratification in the asymptomatic population. Epicardial adipose tissue (EAT), a local visceral fat depot surrounding the heart, has been recently reported as a significant imaging biomarker for cardiovascular risk stratification. Despite being routinely imaged noninvasively by non-contrast cardiac CT for coronary calcium scoring, EAT features are currently not measured or reported, primarily due to the absence of robust, automated quantification methods. We propose to employ novel image processing algorithms to achieve fully automated, robust quantification of EAT features from cardiac CT, and to apply machine learning methods to efficiently combine patient clinical data, coronary calcium and EAT features into a new integrated risk score to predict future cardiac events. We will evaluate this risk score in two existing, prospective registries of asymptomatic patients with available coronary calcium scans, clinical data, and followup for cardiac events (myocardial infarction, cardiac death, late revascularization). These rich resources are: the Early Identification of Subclinical Atherosclerosis Using Non-Invasive Imaging Research [EISNER] registry (2614 patients) and the St. Francis Heart Study (4613 patients). We propose three specific aims: 1) To develop and evaluate the accuracy and reproducibility of new computational algorithms for automated quantification of EAT measures; 2) To evaluate the prognostic value of automatically-quantified EAT features, relative to atherosclerotic plaque burden and measures of obesity, for the prediction of future cardiac events in asymptomatic patients in the EISNER registry and the St. Francis Heart Study; 3) To derive and evaluate a new, integrated risk score?combining standard risk factors, coronary calcium score and EAT measures using machine learning?for the prediction of future cardiac events in the EISNER registry, and further, to additionally evaluate this risk score externally in the St. Francis Heart Study patient cohort. This work in this proposal will provide a novel, personalized paradigm that will objectively and accurately identify asymptomatic patients undergoing coronary calcium scanning who are at increased risk for future cardiac events, without any additional imaging or risk to the patient.